{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b75bf94a",
   "metadata": {},
   "source": [
    "## EfficientNet V2 相较 V1 的改进点\n",
    "\n",
    "* 网络架构搜索的损失函数考虑了精度,参数数量以及训练速度\n",
    "\n",
    "* 浅层引入 Fused-MBConv 模块. 在浅层网络（Stage 1-3 或 Stage 1-4）中，将传统的 **MBConv 模块** 替换为 **Fused-MBConv**（融合倒置残差块），通过合并深度可分离卷积（Depthwise Conv）和扩展层操作，减少计算延迟并提升浅层特征提取效率\n",
    "\n",
    "<img src=\"resources/efficientnetv2_block.png\" alt=\"drawing\" width=\"40%\"/>\n",
    "\n",
    "* 引入了Progressive Training的方法, 训练过程中逐步调整方法图像的大小,并逐步加大正则项控制模型复杂度\n",
    "\n",
    "<img src=\"resources/efficientnetv2_pareto.png\" alt=\"drawing\" width=\"60%\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72e09dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb715028",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30733570",
   "metadata": {},
   "source": [
    "# 使用单一图像大小进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "072062de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 20190298\n",
      "Epoch: 1/100 Train Loss: 2.7455 Accuracy: 0.1044 Time: 16.69262  | Val Loss: 2.3202 Accuracy: 0.1022\n",
      "Epoch: 2/100 Train Loss: 2.4209 Accuracy: 0.1098 Time: 16.62807  | Val Loss: 2.3295 Accuracy: 0.1307\n",
      "Epoch: 3/100 Train Loss: 2.3527 Accuracy: 0.1312 Time: 16.37806  | Val Loss: 2.2494 Accuracy: 0.1857\n",
      "Epoch: 4/100 Train Loss: 2.2598 Accuracy: 0.1766 Time: 16.35645  | Val Loss: 2.1056 Accuracy: 0.2395\n",
      "Epoch: 5/100 Train Loss: 2.1671 Accuracy: 0.2291 Time: 16.59511  | Val Loss: 2.0084 Accuracy: 0.3118\n",
      "Epoch: 6/100 Train Loss: 2.0891 Accuracy: 0.2738 Time: 16.62948  | Val Loss: 1.9040 Accuracy: 0.3870\n",
      "Epoch: 7/100 Train Loss: 2.0156 Accuracy: 0.3204 Time: 16.63088  | Val Loss: 1.8478 Accuracy: 0.4245\n",
      "Epoch: 8/100 Train Loss: 1.9281 Accuracy: 0.3736 Time: 16.69632  | Val Loss: 1.7659 Accuracy: 0.4494\n",
      "Epoch: 9/100 Train Loss: 1.8819 Accuracy: 0.3951 Time: 17.03115  | Val Loss: 1.9621 Accuracy: 0.3811\n",
      "Epoch: 10/100 Train Loss: 1.8324 Accuracy: 0.4174 Time: 16.87950  | Val Loss: 1.6804 Accuracy: 0.4973\n",
      "Epoch: 11/100 Train Loss: 1.7815 Accuracy: 0.4463 Time: 16.89272  | Val Loss: 1.8638 Accuracy: 0.4102\n",
      "Epoch: 12/100 Train Loss: 1.7046 Accuracy: 0.4847 Time: 16.96795  | Val Loss: 1.5194 Accuracy: 0.5620\n",
      "Epoch: 13/100 Train Loss: 1.6598 Accuracy: 0.5096 Time: 17.40665  | Val Loss: 1.4502 Accuracy: 0.5972\n",
      "Epoch: 14/100 Train Loss: 1.6198 Accuracy: 0.5250 Time: 17.19818  | Val Loss: 1.4377 Accuracy: 0.5982\n",
      "Epoch: 15/100 Train Loss: 1.5633 Accuracy: 0.5456 Time: 16.54992  | Val Loss: 1.4391 Accuracy: 0.6031\n",
      "Epoch: 16/100 Train Loss: 1.5138 Accuracy: 0.5685 Time: 16.40132  | Val Loss: 1.3551 Accuracy: 0.6392\n",
      "Epoch: 17/100 Train Loss: 1.4845 Accuracy: 0.5830 Time: 16.35435  | Val Loss: 1.3687 Accuracy: 0.6326\n",
      "Epoch: 18/100 Train Loss: 1.4359 Accuracy: 0.6089 Time: 16.37257  | Val Loss: 1.2395 Accuracy: 0.6989\n",
      "Epoch: 19/100 Train Loss: 1.3926 Accuracy: 0.6331 Time: 16.45821  | Val Loss: 1.1898 Accuracy: 0.7197\n",
      "Epoch: 20/100 Train Loss: 1.3709 Accuracy: 0.6411 Time: 16.86359  | Val Loss: 1.1479 Accuracy: 0.7452\n",
      "Epoch: 21/100 Train Loss: 1.3369 Accuracy: 0.6486 Time: 16.26778  | Val Loss: 1.2010 Accuracy: 0.7215\n",
      "Epoch: 22/100 Train Loss: 1.3195 Accuracy: 0.6573 Time: 16.35842  | Val Loss: 1.0904 Accuracy: 0.7592\n",
      "Epoch: 23/100 Train Loss: 1.2927 Accuracy: 0.6668 Time: 16.34406  | Val Loss: 1.1434 Accuracy: 0.7478\n",
      "Epoch: 24/100 Train Loss: 1.2644 Accuracy: 0.6890 Time: 16.23892  | Val Loss: 1.0847 Accuracy: 0.7577\n",
      "Epoch: 25/100 Train Loss: 1.2487 Accuracy: 0.6938 Time: 16.22336  | Val Loss: 1.1353 Accuracy: 0.7455\n",
      "Epoch: 26/100 Train Loss: 1.2192 Accuracy: 0.6989 Time: 16.31279  | Val Loss: 1.0536 Accuracy: 0.7796\n",
      "Epoch: 27/100 Train Loss: 1.2042 Accuracy: 0.7113 Time: 16.31862  | Val Loss: 1.0345 Accuracy: 0.7893\n",
      "Epoch: 28/100 Train Loss: 1.1891 Accuracy: 0.7134 Time: 16.83451  | Val Loss: 1.0994 Accuracy: 0.7562\n",
      "Epoch: 29/100 Train Loss: 1.1719 Accuracy: 0.7308 Time: 16.72646  | Val Loss: 0.9823 Accuracy: 0.8110\n",
      "Epoch: 30/100 Train Loss: 1.1605 Accuracy: 0.7325 Time: 16.78494  | Val Loss: 1.0071 Accuracy: 0.7957\n",
      "Epoch: 31/100 Train Loss: 1.1369 Accuracy: 0.7421 Time: 16.75835  | Val Loss: 0.9750 Accuracy: 0.8158\n",
      "Epoch: 32/100 Train Loss: 1.1216 Accuracy: 0.7483 Time: 16.56059  | Val Loss: 0.9740 Accuracy: 0.8117\n",
      "Epoch: 33/100 Train Loss: 1.1074 Accuracy: 0.7568 Time: 16.70395  | Val Loss: 0.9627 Accuracy: 0.8181\n",
      "Epoch: 34/100 Train Loss: 1.0947 Accuracy: 0.7605 Time: 16.71309  | Val Loss: 0.9623 Accuracy: 0.8132\n",
      "Epoch: 35/100 Train Loss: 1.0830 Accuracy: 0.7679 Time: 16.67292  | Val Loss: 0.9467 Accuracy: 0.8166\n",
      "Epoch: 36/100 Train Loss: 1.0689 Accuracy: 0.7707 Time: 16.59056  | Val Loss: 0.9085 Accuracy: 0.8344\n",
      "Epoch: 37/100 Train Loss: 1.0670 Accuracy: 0.7708 Time: 16.36521  | Val Loss: 0.9475 Accuracy: 0.8135\n",
      "Epoch: 38/100 Train Loss: 1.0549 Accuracy: 0.7815 Time: 16.52857  | Val Loss: 0.9090 Accuracy: 0.8367\n",
      "Epoch: 39/100 Train Loss: 1.0259 Accuracy: 0.7903 Time: 16.41437  | Val Loss: 0.8929 Accuracy: 0.8459\n",
      "Epoch: 40/100 Train Loss: 1.0221 Accuracy: 0.7909 Time: 16.45118  | Val Loss: 0.9137 Accuracy: 0.8395\n",
      "Epoch: 41/100 Train Loss: 1.0059 Accuracy: 0.7933 Time: 16.33961  | Val Loss: 0.9255 Accuracy: 0.8273\n",
      "Epoch: 42/100 Train Loss: 1.0026 Accuracy: 0.8006 Time: 16.93613  | Val Loss: 0.8703 Accuracy: 0.8576\n",
      "Epoch: 43/100 Train Loss: 0.9774 Accuracy: 0.8098 Time: 16.72986  | Val Loss: 0.8988 Accuracy: 0.8441\n",
      "Epoch: 44/100 Train Loss: 0.9863 Accuracy: 0.8086 Time: 16.38476  | Val Loss: 0.8752 Accuracy: 0.8492\n",
      "Epoch: 45/100 Train Loss: 0.9770 Accuracy: 0.8071 Time: 16.45148  | Val Loss: 0.8514 Accuracy: 0.8596\n",
      "Epoch: 46/100 Train Loss: 0.9578 Accuracy: 0.8160 Time: 16.49500  | Val Loss: 0.8380 Accuracy: 0.8624\n",
      "Epoch: 47/100 Train Loss: 0.9468 Accuracy: 0.8231 Time: 16.51257  | Val Loss: 0.8508 Accuracy: 0.8619\n",
      "Epoch: 48/100 Train Loss: 0.9348 Accuracy: 0.8306 Time: 16.51119  | Val Loss: 0.8780 Accuracy: 0.8512\n",
      "Epoch: 49/100 Train Loss: 0.9330 Accuracy: 0.8252 Time: 16.25968  | Val Loss: 0.8410 Accuracy: 0.8665\n",
      "Epoch: 50/100 Train Loss: 0.9091 Accuracy: 0.8389 Time: 16.34290  | Val Loss: 0.8151 Accuracy: 0.8708\n",
      "Epoch: 51/100 Train Loss: 0.9169 Accuracy: 0.8344 Time: 16.28877  | Val Loss: 0.8464 Accuracy: 0.8627\n",
      "Epoch: 52/100 Train Loss: 0.9033 Accuracy: 0.8395 Time: 16.30550  | Val Loss: 0.8426 Accuracy: 0.8611\n",
      "Epoch: 53/100 Train Loss: 0.8968 Accuracy: 0.8432 Time: 16.37868  | Val Loss: 0.8291 Accuracy: 0.8701\n",
      "Epoch: 54/100 Train Loss: 0.8831 Accuracy: 0.8479 Time: 16.18777  | Val Loss: 0.7980 Accuracy: 0.8803\n",
      "Epoch: 55/100 Train Loss: 0.8816 Accuracy: 0.8481 Time: 16.27060  | Val Loss: 0.7956 Accuracy: 0.8803\n",
      "Epoch: 56/100 Train Loss: 0.8600 Accuracy: 0.8586 Time: 16.19239  | Val Loss: 0.8115 Accuracy: 0.8741\n",
      "Epoch: 57/100 Train Loss: 0.8638 Accuracy: 0.8524 Time: 16.19899  | Val Loss: 0.7823 Accuracy: 0.8882\n",
      "Epoch: 58/100 Train Loss: 0.8448 Accuracy: 0.8614 Time: 16.34957  | Val Loss: 0.7858 Accuracy: 0.8859\n",
      "Epoch: 59/100 Train Loss: 0.8307 Accuracy: 0.8718 Time: 16.38914  | Val Loss: 0.7708 Accuracy: 0.8917\n",
      "Epoch: 60/100 Train Loss: 0.8354 Accuracy: 0.8667 Time: 16.21132  | Val Loss: 0.7730 Accuracy: 0.8899\n",
      "Epoch: 61/100 Train Loss: 0.8239 Accuracy: 0.8722 Time: 16.50996  | Val Loss: 0.7628 Accuracy: 0.8966\n",
      "Epoch: 62/100 Train Loss: 0.8137 Accuracy: 0.8741 Time: 16.69847  | Val Loss: 0.7662 Accuracy: 0.8935\n",
      "Epoch: 63/100 Train Loss: 0.8055 Accuracy: 0.8801 Time: 16.37671  | Val Loss: 0.7730 Accuracy: 0.8943\n",
      "Epoch: 64/100 Train Loss: 0.7949 Accuracy: 0.8836 Time: 16.32690  | Val Loss: 0.7642 Accuracy: 0.8943\n",
      "Epoch: 65/100 Train Loss: 0.7876 Accuracy: 0.8851 Time: 16.44082  | Val Loss: 0.7594 Accuracy: 0.8930\n",
      "Epoch: 66/100 Train Loss: 0.7862 Accuracy: 0.8877 Time: 16.28553  | Val Loss: 0.7678 Accuracy: 0.8973\n",
      "Epoch: 67/100 Train Loss: 0.7756 Accuracy: 0.8905 Time: 16.26622  | Val Loss: 0.7656 Accuracy: 0.8930\n",
      "Epoch: 68/100 Train Loss: 0.7666 Accuracy: 0.8964 Time: 16.28319  | Val Loss: 0.7582 Accuracy: 0.8963\n",
      "Epoch: 69/100 Train Loss: 0.7637 Accuracy: 0.8962 Time: 16.29440  | Val Loss: 0.7622 Accuracy: 0.8930\n",
      "Epoch: 70/100 Train Loss: 0.7518 Accuracy: 0.8993 Time: 16.20651  | Val Loss: 0.7654 Accuracy: 0.8912\n",
      "Epoch: 71/100 Train Loss: 0.7404 Accuracy: 0.9065 Time: 16.26371  | Val Loss: 0.7479 Accuracy: 0.8955\n",
      "Epoch: 72/100 Train Loss: 0.7487 Accuracy: 0.9001 Time: 16.23733  | Val Loss: 0.7508 Accuracy: 0.8968\n",
      "Epoch: 73/100 Train Loss: 0.7301 Accuracy: 0.9125 Time: 16.40357  | Val Loss: 0.7457 Accuracy: 0.8999\n",
      "Epoch: 74/100 Train Loss: 0.7351 Accuracy: 0.9083 Time: 16.71103  | Val Loss: 0.7424 Accuracy: 0.9039\n",
      "Epoch: 75/100 Train Loss: 0.7140 Accuracy: 0.9159 Time: 16.67303  | Val Loss: 0.7274 Accuracy: 0.9070\n",
      "Epoch: 76/100 Train Loss: 0.7212 Accuracy: 0.9142 Time: 16.75301  | Val Loss: 0.7295 Accuracy: 0.9083\n",
      "Epoch: 77/100 Train Loss: 0.7090 Accuracy: 0.9202 Time: 16.73561  | Val Loss: 0.7400 Accuracy: 0.9022\n",
      "Epoch: 78/100 Train Loss: 0.7007 Accuracy: 0.9219 Time: 16.60645  | Val Loss: 0.7299 Accuracy: 0.9057\n",
      "Epoch: 79/100 Train Loss: 0.6973 Accuracy: 0.9234 Time: 16.54192  | Val Loss: 0.7341 Accuracy: 0.9047\n",
      "Epoch: 80/100 Train Loss: 0.6984 Accuracy: 0.9259 Time: 16.56554  | Val Loss: 0.7264 Accuracy: 0.9060\n",
      "Epoch: 81/100 Train Loss: 0.6889 Accuracy: 0.9284 Time: 16.60857  | Val Loss: 0.7277 Accuracy: 0.9083\n",
      "Epoch: 82/100 Train Loss: 0.6864 Accuracy: 0.9283 Time: 16.72884  | Val Loss: 0.7275 Accuracy: 0.9078\n",
      "Epoch: 83/100 Train Loss: 0.6816 Accuracy: 0.9299 Time: 16.87928  | Val Loss: 0.7274 Accuracy: 0.9075\n",
      "Epoch: 84/100 Train Loss: 0.6736 Accuracy: 0.9351 Time: 16.68693  | Val Loss: 0.7174 Accuracy: 0.9124\n",
      "Epoch: 85/100 Train Loss: 0.6812 Accuracy: 0.9314 Time: 16.69874  | Val Loss: 0.7216 Accuracy: 0.9068\n",
      "Epoch: 86/100 Train Loss: 0.6681 Accuracy: 0.9357 Time: 16.73928  | Val Loss: 0.7206 Accuracy: 0.9134\n",
      "Epoch: 87/100 Train Loss: 0.6691 Accuracy: 0.9360 Time: 16.79829  | Val Loss: 0.7261 Accuracy: 0.9073\n",
      "Epoch: 88/100 Train Loss: 0.6648 Accuracy: 0.9362 Time: 16.63298  | Val Loss: 0.7221 Accuracy: 0.9103\n",
      "Epoch: 89/100 Train Loss: 0.6569 Accuracy: 0.9436 Time: 16.69563  | Val Loss: 0.7253 Accuracy: 0.9088\n",
      "Epoch: 90/100 Train Loss: 0.6613 Accuracy: 0.9380 Time: 16.62103  | Val Loss: 0.7180 Accuracy: 0.9111\n",
      "Epoch: 91/100 Train Loss: 0.6558 Accuracy: 0.9411 Time: 16.32117  | Val Loss: 0.7226 Accuracy: 0.9139\n",
      "Epoch: 92/100 Train Loss: 0.6570 Accuracy: 0.9392 Time: 16.26244  | Val Loss: 0.7186 Accuracy: 0.9146\n",
      "Epoch: 93/100 Train Loss: 0.6477 Accuracy: 0.9438 Time: 16.30417  | Val Loss: 0.7175 Accuracy: 0.9118\n",
      "Epoch: 94/100 Train Loss: 0.6491 Accuracy: 0.9454 Time: 16.25197  | Val Loss: 0.7183 Accuracy: 0.9118\n",
      "Epoch: 95/100 Train Loss: 0.6502 Accuracy: 0.9425 Time: 16.34641  | Val Loss: 0.7179 Accuracy: 0.9141\n",
      "Epoch: 96/100 Train Loss: 0.6515 Accuracy: 0.9408 Time: 16.28389  | Val Loss: 0.7167 Accuracy: 0.9141\n",
      "Epoch: 97/100 Train Loss: 0.6471 Accuracy: 0.9424 Time: 16.30423  | Val Loss: 0.7173 Accuracy: 0.9154\n",
      "Epoch: 98/100 Train Loss: 0.6449 Accuracy: 0.9455 Time: 16.26349  | Val Loss: 0.7166 Accuracy: 0.9141\n",
      "Epoch: 99/100 Train Loss: 0.6456 Accuracy: 0.9454 Time: 16.28085  | Val Loss: 0.7123 Accuracy: 0.9164\n",
      "Epoch: 100/100 Train Loss: 0.6440 Accuracy: 0.9466 Time: 16.40889  | Val Loss: 0.7124 Accuracy: 0.9172\n"
     ]
    }
   ],
   "source": [
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from hdd.models.cnn.efficientnet import create_efficient_net\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "MAX_EPOCHES = 100\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    net,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    scheduler,\n",
    "    optimizer,\n",
    "    max_epochs=100,\n",
    ") -> dict[str, list[float]]:\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return training_stats\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader\n",
    "\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "net, _, _ = create_efficient_net(\"efficientnet_v2_s\", 0.5, 10)\n",
    "net = net.to(DEVICE)\n",
    "\n",
    "crop_size = 224\n",
    "resize_size = crop_size + 30\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(resize_size),\n",
    "        transforms.RandomCrop(crop_size),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        ImageNetPolicy(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset.transform = train_dataset_transforms\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(resize_size),\n",
    "        transforms.CenterCrop(crop_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset.transform = val_dataset_transforms\n",
    "train_dataloader, val_dataloader = build_dataloader(\n",
    "    BATCH_SIZE, train_dataset, val_dataset\n",
    ")\n",
    "lr = 0.005\n",
    "weight_decay = 1e-2\n",
    "optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "    optimizer, MAX_EPOCHES, eta_min=lr / 100\n",
    ")\n",
    "one_size_stats = train_net(\n",
    "    net,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    scheduler,\n",
    "    optimizer,\n",
    "    max_epochs=MAX_EPOCHES,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f83778b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 20190298\n",
      "Epoch: 1/25 Train Loss: 3.1068 Accuracy: 0.1185 Time: 9.02530  | Val Loss: 3.9094 Accuracy: 0.1210\n",
      "Epoch: 2/25 Train Loss: 2.5543 Accuracy: 0.1364 Time: 9.16927  | Val Loss: 2.2154 Accuracy: 0.1990\n",
      "Epoch: 3/25 Train Loss: 2.4415 Accuracy: 0.1746 Time: 9.19316  | Val Loss: 2.2969 Accuracy: 0.1982\n",
      "Epoch: 4/25 Train Loss: 2.4267 Accuracy: 0.1747 Time: 8.84097  | Val Loss: 2.2542 Accuracy: 0.2318\n",
      "Epoch: 5/25 Train Loss: 2.3877 Accuracy: 0.1939 Time: 9.25567  | Val Loss: 2.2947 Accuracy: 0.2540\n",
      "Epoch: 6/25 Train Loss: 2.3379 Accuracy: 0.2060 Time: 8.96851  | Val Loss: 2.2267 Accuracy: 0.3039\n",
      "Epoch: 7/25 Train Loss: 2.2778 Accuracy: 0.2441 Time: 9.08364  | Val Loss: 2.0426 Accuracy: 0.3090\n",
      "Epoch: 8/25 Train Loss: 2.2652 Accuracy: 0.2520 Time: 9.03285  | Val Loss: 2.1540 Accuracy: 0.3101\n",
      "Epoch: 9/25 Train Loss: 2.2413 Accuracy: 0.2535 Time: 9.06584  | Val Loss: 2.0088 Accuracy: 0.3266\n",
      "Epoch: 10/25 Train Loss: 2.1209 Accuracy: 0.2909 Time: 9.05222  | Val Loss: 1.9242 Accuracy: 0.3817\n",
      "Epoch: 11/25 Train Loss: 2.0259 Accuracy: 0.3296 Time: 8.90021  | Val Loss: 1.7948 Accuracy: 0.4413\n",
      "Epoch: 12/25 Train Loss: 1.9535 Accuracy: 0.3634 Time: 9.35463  | Val Loss: 1.7283 Accuracy: 0.4769\n",
      "Epoch: 13/25 Train Loss: 1.8800 Accuracy: 0.4053 Time: 8.92868  | Val Loss: 1.6014 Accuracy: 0.5506\n",
      "Epoch: 14/25 Train Loss: 1.8480 Accuracy: 0.4113 Time: 8.97375  | Val Loss: 1.5414 Accuracy: 0.5669\n",
      "Epoch: 15/25 Train Loss: 1.7976 Accuracy: 0.4405 Time: 8.99246  | Val Loss: 1.6553 Accuracy: 0.4981\n",
      "Epoch: 16/25 Train Loss: 1.7757 Accuracy: 0.4456 Time: 9.20931  | Val Loss: 1.4735 Accuracy: 0.5822\n",
      "Epoch: 17/25 Train Loss: 1.7125 Accuracy: 0.4820 Time: 8.96861  | Val Loss: 1.5442 Accuracy: 0.5946\n",
      "Epoch: 18/25 Train Loss: 1.6720 Accuracy: 0.4975 Time: 8.95176  | Val Loss: 1.5060 Accuracy: 0.6074\n",
      "Epoch: 19/25 Train Loss: 1.6649 Accuracy: 0.5041 Time: 9.29450  | Val Loss: 1.4283 Accuracy: 0.6324\n",
      "Epoch: 20/25 Train Loss: 1.6032 Accuracy: 0.5302 Time: 8.94854  | Val Loss: 1.5550 Accuracy: 0.5524\n",
      "Epoch: 21/25 Train Loss: 1.5939 Accuracy: 0.5319 Time: 8.74883  | Val Loss: 1.4068 Accuracy: 0.6436\n",
      "Epoch: 22/25 Train Loss: 1.5392 Accuracy: 0.5600 Time: 8.47849  | Val Loss: 1.3317 Accuracy: 0.6637\n",
      "Epoch: 23/25 Train Loss: 1.5160 Accuracy: 0.5679 Time: 8.56104  | Val Loss: 1.2613 Accuracy: 0.6846\n",
      "Epoch: 24/25 Train Loss: 1.4908 Accuracy: 0.5826 Time: 8.75592  | Val Loss: 1.2297 Accuracy: 0.7001\n",
      "Epoch: 25/25 Train Loss: 1.4595 Accuracy: 0.5903 Time: 8.86366  | Val Loss: 1.2650 Accuracy: 0.6815\n",
      "#Parameter: 20190298\n",
      "Epoch: 1/25 Train Loss: 1.4334 Accuracy: 0.6030 Time: 10.82589  | Val Loss: 1.1823 Accuracy: 0.7210\n",
      "Epoch: 2/25 Train Loss: 1.3847 Accuracy: 0.6285 Time: 10.18587  | Val Loss: 1.1369 Accuracy: 0.7391\n",
      "Epoch: 3/25 Train Loss: 1.3389 Accuracy: 0.6501 Time: 10.29918  | Val Loss: 1.1504 Accuracy: 0.7521\n",
      "Epoch: 4/25 Train Loss: 1.3095 Accuracy: 0.6597 Time: 10.18286  | Val Loss: 1.0897 Accuracy: 0.7536\n",
      "Epoch: 5/25 Train Loss: 1.3063 Accuracy: 0.6617 Time: 10.17164  | Val Loss: 1.0946 Accuracy: 0.7559\n",
      "Epoch: 6/25 Train Loss: 1.2806 Accuracy: 0.6716 Time: 10.29897  | Val Loss: 1.0371 Accuracy: 0.7748\n",
      "Epoch: 7/25 Train Loss: 1.2476 Accuracy: 0.6912 Time: 10.28157  | Val Loss: 1.0814 Accuracy: 0.7597\n",
      "Epoch: 8/25 Train Loss: 1.2466 Accuracy: 0.6855 Time: 10.26883  | Val Loss: 1.0304 Accuracy: 0.7796\n",
      "Epoch: 9/25 Train Loss: 1.2351 Accuracy: 0.6934 Time: 10.16705  | Val Loss: 1.0812 Accuracy: 0.7758\n",
      "Epoch: 10/25 Train Loss: 1.2308 Accuracy: 0.6952 Time: 10.50807  | Val Loss: 1.0121 Accuracy: 0.7791\n",
      "Epoch: 11/25 Train Loss: 1.1905 Accuracy: 0.7138 Time: 10.30558  | Val Loss: 0.9957 Accuracy: 0.7964\n",
      "Epoch: 12/25 Train Loss: 1.1750 Accuracy: 0.7182 Time: 10.52923  | Val Loss: 1.0336 Accuracy: 0.7806\n",
      "Epoch: 13/25 Train Loss: 1.1934 Accuracy: 0.7104 Time: 10.55731  | Val Loss: 1.0097 Accuracy: 0.7931\n",
      "Epoch: 14/25 Train Loss: 1.1658 Accuracy: 0.7206 Time: 10.30469  | Val Loss: 0.9751 Accuracy: 0.8003\n",
      "Epoch: 15/25 Train Loss: 1.1568 Accuracy: 0.7245 Time: 10.36012  | Val Loss: 0.9674 Accuracy: 0.8020\n",
      "Epoch: 16/25 Train Loss: 1.1165 Accuracy: 0.7385 Time: 10.44067  | Val Loss: 0.9658 Accuracy: 0.8094\n",
      "Epoch: 17/25 Train Loss: 1.1153 Accuracy: 0.7405 Time: 10.25005  | Val Loss: 0.9722 Accuracy: 0.8028\n",
      "Epoch: 18/25 Train Loss: 1.1130 Accuracy: 0.7410 Time: 10.36553  | Val Loss: 1.0263 Accuracy: 0.7865\n",
      "Epoch: 19/25 Train Loss: 1.0966 Accuracy: 0.7544 Time: 10.49114  | Val Loss: 0.9454 Accuracy: 0.8153\n",
      "Epoch: 20/25 Train Loss: 1.0715 Accuracy: 0.7615 Time: 10.37530  | Val Loss: 0.9286 Accuracy: 0.8224\n",
      "Epoch: 21/25 Train Loss: 1.0512 Accuracy: 0.7726 Time: 10.41184  | Val Loss: 0.9152 Accuracy: 0.8331\n",
      "Epoch: 22/25 Train Loss: 1.0579 Accuracy: 0.7652 Time: 10.09830  | Val Loss: 0.9255 Accuracy: 0.8303\n",
      "Epoch: 23/25 Train Loss: 1.0405 Accuracy: 0.7738 Time: 9.99776  | Val Loss: 0.9502 Accuracy: 0.8204\n",
      "Epoch: 24/25 Train Loss: 1.0204 Accuracy: 0.7826 Time: 10.07600  | Val Loss: 0.9227 Accuracy: 0.8280\n",
      "Epoch: 25/25 Train Loss: 1.0097 Accuracy: 0.7882 Time: 10.12204  | Val Loss: 0.9099 Accuracy: 0.8375\n",
      "#Parameter: 20190298\n",
      "Epoch: 1/25 Train Loss: 0.9774 Accuracy: 0.8018 Time: 12.69759  | Val Loss: 0.8678 Accuracy: 0.8504\n",
      "Epoch: 2/25 Train Loss: 0.9526 Accuracy: 0.8161 Time: 12.67085  | Val Loss: 0.8714 Accuracy: 0.8502\n",
      "Epoch: 3/25 Train Loss: 0.9519 Accuracy: 0.8048 Time: 12.58180  | Val Loss: 0.8686 Accuracy: 0.8558\n",
      "Epoch: 4/25 Train Loss: 0.9436 Accuracy: 0.8187 Time: 12.61478  | Val Loss: 0.8451 Accuracy: 0.8634\n",
      "Epoch: 5/25 Train Loss: 0.9275 Accuracy: 0.8230 Time: 12.63611  | Val Loss: 0.8419 Accuracy: 0.8596\n",
      "Epoch: 6/25 Train Loss: 0.9287 Accuracy: 0.8221 Time: 12.64124  | Val Loss: 0.9010 Accuracy: 0.8499\n",
      "Epoch: 7/25 Train Loss: 0.9313 Accuracy: 0.8231 Time: 12.59472  | Val Loss: 0.8593 Accuracy: 0.8525\n",
      "Epoch: 8/25 Train Loss: 0.9207 Accuracy: 0.8249 Time: 12.66818  | Val Loss: 0.8340 Accuracy: 0.8660\n",
      "Epoch: 9/25 Train Loss: 0.9116 Accuracy: 0.8338 Time: 12.59567  | Val Loss: 0.8320 Accuracy: 0.8652\n",
      "Epoch: 10/25 Train Loss: 0.8948 Accuracy: 0.8407 Time: 12.62863  | Val Loss: 0.8312 Accuracy: 0.8708\n",
      "Epoch: 11/25 Train Loss: 0.8855 Accuracy: 0.8395 Time: 12.70036  | Val Loss: 0.8406 Accuracy: 0.8634\n",
      "Epoch: 12/25 Train Loss: 0.8769 Accuracy: 0.8480 Time: 12.55817  | Val Loss: 0.8271 Accuracy: 0.8698\n",
      "Epoch: 13/25 Train Loss: 0.8723 Accuracy: 0.8462 Time: 12.63673  | Val Loss: 0.8254 Accuracy: 0.8701\n",
      "Epoch: 14/25 Train Loss: 0.8773 Accuracy: 0.8435 Time: 12.63604  | Val Loss: 0.8221 Accuracy: 0.8690\n",
      "Epoch: 15/25 Train Loss: 0.8583 Accuracy: 0.8542 Time: 12.82710  | Val Loss: 0.8100 Accuracy: 0.8721\n",
      "Epoch: 16/25 Train Loss: 0.8567 Accuracy: 0.8544 Time: 12.90600  | Val Loss: 0.8237 Accuracy: 0.8685\n",
      "Epoch: 17/25 Train Loss: 0.8482 Accuracy: 0.8526 Time: 12.95872  | Val Loss: 0.8155 Accuracy: 0.8731\n",
      "Epoch: 18/25 Train Loss: 0.8376 Accuracy: 0.8612 Time: 12.87345  | Val Loss: 0.8259 Accuracy: 0.8703\n",
      "Epoch: 19/25 Train Loss: 0.8414 Accuracy: 0.8603 Time: 12.79289  | Val Loss: 0.8227 Accuracy: 0.8729\n",
      "Epoch: 20/25 Train Loss: 0.8281 Accuracy: 0.8662 Time: 12.89619  | Val Loss: 0.8031 Accuracy: 0.8782\n",
      "Epoch: 21/25 Train Loss: 0.8275 Accuracy: 0.8662 Time: 13.04362  | Val Loss: 0.8073 Accuracy: 0.8739\n",
      "Epoch: 22/25 Train Loss: 0.8265 Accuracy: 0.8671 Time: 12.86572  | Val Loss: 0.8270 Accuracy: 0.8706\n",
      "Epoch: 23/25 Train Loss: 0.8093 Accuracy: 0.8779 Time: 12.89877  | Val Loss: 0.8270 Accuracy: 0.8688\n",
      "Epoch: 24/25 Train Loss: 0.8137 Accuracy: 0.8673 Time: 12.82982  | Val Loss: 0.8302 Accuracy: 0.8739\n",
      "Epoch: 25/25 Train Loss: 0.7955 Accuracy: 0.8799 Time: 13.05052  | Val Loss: 0.8288 Accuracy: 0.8739\n",
      "#Parameter: 20190298\n",
      "Epoch: 1/25 Train Loss: 0.7965 Accuracy: 0.8779 Time: 16.76218  | Val Loss: 0.7829 Accuracy: 0.8854\n",
      "Epoch: 2/25 Train Loss: 0.7856 Accuracy: 0.8844 Time: 16.77143  | Val Loss: 0.8017 Accuracy: 0.8846\n",
      "Epoch: 3/25 Train Loss: 0.7737 Accuracy: 0.8888 Time: 16.82417  | Val Loss: 0.8824 Accuracy: 0.8703\n",
      "Epoch: 4/25 Train Loss: 0.7693 Accuracy: 0.8910 Time: 16.69807  | Val Loss: 0.7853 Accuracy: 0.8861\n",
      "Epoch: 5/25 Train Loss: 0.7732 Accuracy: 0.8905 Time: 16.88420  | Val Loss: 0.8263 Accuracy: 0.8828\n",
      "Epoch: 6/25 Train Loss: 0.7782 Accuracy: 0.8853 Time: 16.82053  | Val Loss: 0.7832 Accuracy: 0.8869\n",
      "Epoch: 7/25 Train Loss: 0.7598 Accuracy: 0.8966 Time: 17.01006  | Val Loss: 0.8176 Accuracy: 0.8800\n",
      "Epoch: 8/25 Train Loss: 0.7578 Accuracy: 0.8951 Time: 16.64084  | Val Loss: 0.8369 Accuracy: 0.8792\n",
      "Epoch: 9/25 Train Loss: 0.7435 Accuracy: 0.9010 Time: 16.65514  | Val Loss: 0.7952 Accuracy: 0.8879\n",
      "Epoch: 10/25 Train Loss: 0.7639 Accuracy: 0.8935 Time: 16.94252  | Val Loss: 0.7970 Accuracy: 0.8820\n",
      "Epoch: 11/25 Train Loss: 0.7458 Accuracy: 0.9016 Time: 16.67937  | Val Loss: 0.7857 Accuracy: 0.8912\n",
      "Epoch: 12/25 Train Loss: 0.7481 Accuracy: 0.8965 Time: 16.55105  | Val Loss: 0.8249 Accuracy: 0.8777\n",
      "Epoch: 13/25 Train Loss: 0.7404 Accuracy: 0.9026 Time: 16.43871  | Val Loss: 0.7913 Accuracy: 0.8892\n",
      "Epoch: 14/25 Train Loss: 0.7349 Accuracy: 0.9069 Time: 16.74306  | Val Loss: 0.8035 Accuracy: 0.8874\n",
      "Epoch: 15/25 Train Loss: 0.7282 Accuracy: 0.9078 Time: 16.81251  | Val Loss: 0.8366 Accuracy: 0.8775\n",
      "Epoch: 16/25 Train Loss: 0.7301 Accuracy: 0.9051 Time: 16.75956  | Val Loss: 0.8077 Accuracy: 0.8851\n",
      "Epoch: 17/25 Train Loss: 0.7333 Accuracy: 0.9074 Time: 16.70954  | Val Loss: 0.7965 Accuracy: 0.8871\n",
      "Epoch: 18/25 Train Loss: 0.7215 Accuracy: 0.9103 Time: 16.74782  | Val Loss: 0.7910 Accuracy: 0.8859\n",
      "Epoch: 19/25 Train Loss: 0.7241 Accuracy: 0.9118 Time: 16.73641  | Val Loss: 0.8323 Accuracy: 0.8861\n",
      "Epoch: 20/25 Train Loss: 0.7239 Accuracy: 0.9112 Time: 16.56135  | Val Loss: 0.8036 Accuracy: 0.8876\n",
      "Epoch: 21/25 Train Loss: 0.7081 Accuracy: 0.9172 Time: 16.59680  | Val Loss: 0.7907 Accuracy: 0.8932\n",
      "Epoch: 22/25 Train Loss: 0.7082 Accuracy: 0.9158 Time: 16.59362  | Val Loss: 0.7765 Accuracy: 0.8943\n",
      "Epoch: 23/25 Train Loss: 0.7162 Accuracy: 0.9149 Time: 16.50688  | Val Loss: 0.8105 Accuracy: 0.8897\n",
      "Epoch: 24/25 Train Loss: 0.7062 Accuracy: 0.9175 Time: 16.68139  | Val Loss: 0.7993 Accuracy: 0.8910\n",
      "Epoch: 25/25 Train Loss: 0.7087 Accuracy: 0.9194 Time: 16.80495  | Val Loss: 0.8436 Accuracy: 0.8823\n"
     ]
    }
   ],
   "source": [
    "net, _, _ = create_efficient_net(\"efficientnet_v2_s\", 0.5, 10)\n",
    "net = net.to(DEVICE)\n",
    "crop_sizes = [128, 160, 192, 224]\n",
    "weight_decays = [3e-3, 1e-3, 3e-2, 1e-2]\n",
    "lr = 0.002\n",
    "progressive_learning_stats = []\n",
    "for crop_size, weight_decay in zip(crop_sizes, weight_decays):\n",
    "    train_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(crop_size + 30),\n",
    "            transforms.RandomCrop(crop_size),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            ImageNetPolicy(),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    train_dataset.transform = train_dataset_transforms\n",
    "    val_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(crop_size + 30),\n",
    "            transforms.CenterCrop(crop_size),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    val_dataset.transform = val_dataset_transforms\n",
    "    train_dataloader, val_dataloader = build_dataloader(\n",
    "        BATCH_SIZE, train_dataset, val_dataset\n",
    "    )\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, MAX_EPOCHES // len(crop_sizes), eta_min=lr / 2.5\n",
    "    )\n",
    "    stats = train_net(\n",
    "        net,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        scheduler,\n",
    "        optimizer,\n",
    "        max_epochs=MAX_EPOCHES // len(crop_sizes),\n",
    "    )\n",
    "    progressive_learning_stats.append(stats)\n",
    "    lr = lr / 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7096508b",
   "metadata": {},
   "outputs": [],
   "source": [
    "progressive_learning_stat = {}\n",
    "for field in one_size_stats.keys():\n",
    "    progressive_learning_stat[field] = []\n",
    "    for p in progressive_learning_stats:\n",
    "        progressive_learning_stat[field].extend(p[field])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2599ff46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = one_size_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(progressive_learning_stat[field], label=\"progressive\", linestyle=\"--\")\n",
    "    plt.plot(one_size_stats[field], label=\"non-progressive\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3ba05be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
